Deep Time Reckoning by Vincent Ialenti

Author:Vincent Ialenti
Language: eng
Format: epub
Tags: deep time; Anthropocene; nuclear waste; populism; expertise; climate change; nuclear energy; environmental governance; Finland; ethnography; geological time; political ecology; anti-intellectualism; social media echo-chambers; long-termism; Futures Studies; Discard Studies; environmental security; risk; energy security; forecasting; temporality; sustainability; time literacy; environmental history; Science and Technology Studies; Cultural Anthropology; radioactive waste; Anthropology of Science; Scandinavia; environmental learning; technocracy
Publisher: MIT Press

DEEP TIME IN CHAINS

To the untrained eye of an outsider, each Safety Case model report could seem like a single, standalone document. For Safety Case insiders, though, each report contained models-within-models-within-models that were deeply interconnected. Parts of models were found in other models, which became parts of other models, which became parts of still other models, ad nauseam. To zoom in or out on any section of the Safety Case’s jungles of models, then, was to reveal complex tangles of inputs and outputs. Studying this anthropologically showed the Safety Case’s chains of models to be organized almost like a fractal: a structure or design in which its many individual parts share the same patterns across all sorts of different scales and levels.

The input/output pattern also helped with the organization of the everyday professional workflows between experts. The title “Biosphere Assessment,” for example, referred not only to a bunch of technical documents and modeling reports, but also to the group of Safety Case experts who developed it. The same could be said of “Radionuclide Transport,” “Groundwater Flow,” and other teams. Working relationships between these teams depended on handoffs of information, documents, and models between one another. Once a group finished a model, they would hand it off to another team, which would then input it into their own model, which would then be handed off to still others, who would then input it into a broader-level model, and so on, and so forth. Input/output patterns steered these workflows.

To clarify how this worked, we can return to the example of how the Groundwater Flow model was input into the Radionuclide Transport model, which was then input into the Biosphere Assessment model. In this input/output chain, workplace handoffs of data spreadsheets, paper reports, and PDF files tended to flow from the Radionuclide Transport team toward the Biosphere Assessment team. In other words, handoffs between teams of Safety Case experts followed the routes laid down by the input/output chains that linked together Safety Case models. Both the models and the input/output patterns that organized them were “relationally produced knowledge and knowledge productive of relationality” between teams of people.14

Building on this point, one informant told me how plotting out all of the chains of connections between the inputs and outputs of Posiva’s models would produce a “map” of the total Safety Case portfolio. On this map, any expert could place a “You Are Here” sign locating his or her team’s position within the Safety Case collaboration’s wider jungle of relationships. This gave the portfolio an “all laid out” feel. The layout helped STUK’s regulatory experts navigate it when they reviewed it. This was because a reviewer could track from where the inputs being fed into a model came from and to where the model’s outputs went afterward. If a STUK reviewer wanted to zoom in on a particular Safety Case detail—wondering about, say, microbes’ far future impacts on the repository’s clay buffers—he or she could follow the input/output trail to the answer. This ability to

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